Overview

Dataset statistics

Number of variables10
Number of observations2007
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.9 KiB
Average record size in memory80.1 B

Variable types

Numeric9
Categorical1

Alerts

Solids has unique valuesUnique

Reproduction

Analysis started2023-08-17 19:08:16.554293
Analysis finished2023-08-17 19:08:23.575040
Duration7.02 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ph
Real number (ℝ)

Distinct627
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0885102
Minimum0.23
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:23.650821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile4.623
Q16.09
median7.03
Q38.05
95-th percentile9.797
Maximum14
Range13.77
Interquartile range (IQR)1.96

Descriptive statistics

Standard deviation1.5724527
Coefficient of variation (CV)0.2218312
Kurtosis0.62526285
Mean7.0885102
Median Absolute Deviation (MAD)0.99
Skewness0.050625241
Sum14226.64
Variance2.4726075
MonotonicityNot monotonic
2023-08-17T15:08:23.736479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.51 11
 
0.5%
7.61 11
 
0.5%
7.37 11
 
0.5%
6.58 10
 
0.5%
6.98 10
 
0.5%
6.79 10
 
0.5%
7.29 10
 
0.5%
6.92 10
 
0.5%
6.85 10
 
0.5%
6.34 10
 
0.5%
Other values (617) 1904
94.9%
ValueCountFrequency (%)
0.23 1
< 0.1%
0.99 1
< 0.1%
1.43 1
< 0.1%
1.76 1
< 0.1%
1.99 1
< 0.1%
2.13 1
< 0.1%
2.38 1
< 0.1%
2.54 1
< 0.1%
2.56 1
< 0.1%
2.57 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
13.35 1
< 0.1%
12.25 1
< 0.1%
11.9 1
< 0.1%
11.57 1
< 0.1%
11.56 1
< 0.1%
11.53 1
< 0.1%
11.5 2
0.1%
11.49 1
< 0.1%
11.45 1
< 0.1%

Hardness
Real number (ℝ)

Distinct1819
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.96
Minimum73.49
Maximum317.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:23.830494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum73.49
5-th percentile141.238
Q1176.735
median197.19
Q3216.465
95-th percentile248.851
Maximum317.34
Range243.85
Interquartile range (IQR)39.73

Descriptive statistics

Standard deviation32.662271
Coefficient of variation (CV)0.16667826
Kurtosis0.52235747
Mean195.96
Median Absolute Deviation (MAD)19.94
Skewness-0.084561181
Sum393291.71
Variance1066.8239
MonotonicityNot monotonic
2023-08-17T15:08:23.915281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208.91 4
 
0.2%
169.4 3
 
0.1%
166.64 3
 
0.1%
205.21 3
 
0.1%
203.4 3
 
0.1%
203.07 3
 
0.1%
185.34 3
 
0.1%
200.71 3
 
0.1%
227.23 3
 
0.1%
234.78 3
 
0.1%
Other values (1809) 1976
98.5%
ValueCountFrequency (%)
73.49 1
< 0.1%
77.46 1
< 0.1%
81.71 1
< 0.1%
94.09 1
< 0.1%
94.81 1
< 0.1%
94.91 1
< 0.1%
97.28 1
< 0.1%
98.45 1
< 0.1%
98.77 1
< 0.1%
100.46 1
< 0.1%
ValueCountFrequency (%)
317.34 1
< 0.1%
306.63 1
< 0.1%
300.29 1
< 0.1%
287.98 1
< 0.1%
286.57 1
< 0.1%
284 1
< 0.1%
283.9 1
< 0.1%
282.74 1
< 0.1%
281.59 1
< 0.1%
280.09 1
< 0.1%

Solids
Real number (ℝ)

Distinct2007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21918.672
Minimum320.94
Maximum56488.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:23.991328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum320.94
5-th percentile9550.156
Q115615.665
median20933.51
Q327195.355
95-th percentile38304.702
Maximum56488.67
Range56167.73
Interquartile range (IQR)11579.69

Descriptive statistics

Standard deviation8648.7693
Coefficient of variation (CV)0.39458454
Kurtosis0.34105125
Mean21918.672
Median Absolute Deviation (MAD)5760.25
Skewness0.59549119
Sum43990775
Variance74801210
MonotonicityNot monotonic
2023-08-17T15:08:24.071478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19451.77 1
 
< 0.1%
19598.86 1
 
< 0.1%
14464.12 1
 
< 0.1%
20229.11 1
 
< 0.1%
27776.9 1
 
< 0.1%
12145.54 1
 
< 0.1%
16233.13 1
 
< 0.1%
16559.88 1
 
< 0.1%
11619.71 1
 
< 0.1%
25419.77 1
 
< 0.1%
Other values (1997) 1997
99.5%
ValueCountFrequency (%)
320.94 1
< 0.1%
1198.94 1
< 0.1%
1351.91 1
< 0.1%
1372.09 1
< 0.1%
2552.96 1
< 0.1%
3413.08 1
< 0.1%
3640.73 1
< 0.1%
4111.79 1
< 0.1%
4168.2 1
< 0.1%
4304.49 1
< 0.1%
ValueCountFrequency (%)
56488.67 1
< 0.1%
56351.4 1
< 0.1%
55334.7 1
< 0.1%
53735.9 1
< 0.1%
50793.9 1
< 0.1%
50279.26 1
< 0.1%
49074.73 1
< 0.1%
49009.92 1
< 0.1%
48621.56 1
< 0.1%
48204.17 1
< 0.1%

Chloramines
Real number (ℝ)

Distinct638
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1375386
Minimum1.39
Maximum13.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:24.166184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile4.553
Q16.15
median7.15
Q38.11
95-th percentile9.757
Maximum13.13
Range11.74
Interquartile range (IQR)1.96

Descriptive statistics

Standard deviation1.5844607
Coefficient of variation (CV)0.22198978
Kurtosis0.55684227
Mean7.1375386
Median Absolute Deviation (MAD)0.98
Skewness0.010101841
Sum14325.04
Variance2.5105156
MonotonicityNot monotonic
2023-08-17T15:08:24.244862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.61 11
 
0.5%
7.49 11
 
0.5%
7.63 11
 
0.5%
7.84 11
 
0.5%
7.3 10
 
0.5%
7.69 10
 
0.5%
7.4 10
 
0.5%
7.57 10
 
0.5%
6.19 10
 
0.5%
7.66 10
 
0.5%
Other values (628) 1903
94.8%
ValueCountFrequency (%)
1.39 1
< 0.1%
1.92 1
< 0.1%
2.4 1
< 0.1%
2.46 2
0.1%
2.48 1
< 0.1%
2.5 1
< 0.1%
2.62 1
< 0.1%
2.65 2
0.1%
2.73 1
< 0.1%
2.74 1
< 0.1%
ValueCountFrequency (%)
13.13 1
< 0.1%
13.04 1
< 0.1%
12.65 1
< 0.1%
12.63 1
< 0.1%
12.58 1
< 0.1%
12.25 1
< 0.1%
12.23 1
< 0.1%
12.06 1
< 0.1%
11.99 1
< 0.1%
11.93 1
< 0.1%

Sulfate
Real number (ℝ)

Distinct1869
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.24903
Minimum129
Maximum481.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:24.338539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile267.697
Q1307.63
median332.23
Q3359.4
95-th percentile401.599
Maximum481.03
Range352.03
Interquartile range (IQR)51.77

Descriptive statistics

Standard deviation41.232106
Coefficient of variation (CV)0.12372761
Kurtosis0.78422655
Mean333.24903
Median Absolute Deviation (MAD)25.73
Skewness-0.047571658
Sum668830.81
Variance1700.0866
MonotonicityNot monotonic
2023-08-17T15:08:24.418955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
319.25 3
 
0.1%
320.26 3
 
0.1%
343.29 3
 
0.1%
339.06 3
 
0.1%
340.98 3
 
0.1%
318.79 3
 
0.1%
367.33 2
 
0.1%
322.1 2
 
0.1%
338.58 2
 
0.1%
338.05 2
 
0.1%
Other values (1859) 1981
98.7%
ValueCountFrequency (%)
129 1
< 0.1%
180.21 1
< 0.1%
182.4 1
< 0.1%
187.17 1
< 0.1%
187.42 1
< 0.1%
192.03 1
< 0.1%
203.44 1
< 0.1%
205.94 1
< 0.1%
206.25 1
< 0.1%
207.89 1
< 0.1%
ValueCountFrequency (%)
481.03 1
< 0.1%
476.54 1
< 0.1%
475.74 1
< 0.1%
460.11 1
< 0.1%
458.44 1
< 0.1%
450.91 1
< 0.1%
447.42 1
< 0.1%
446.72 1
< 0.1%
445.94 1
< 0.1%
445.36 1
< 0.1%

Conductivity
Real number (ℝ)

Distinct1934
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426.52422
Minimum201.62
Maximum753.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:24.512317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum201.62
5-th percentile300.452
Q1366.68
median423.42
Q3482.525
95-th percentile564.748
Maximum753.34
Range551.72
Interquartile range (IQR)115.845

Descriptive statistics

Standard deviation80.761753
Coefficient of variation (CV)0.18934857
Kurtosis-0.24136586
Mean426.52422
Median Absolute Deviation (MAD)57.94
Skewness0.26746326
Sum856034.11
Variance6522.4607
MonotonicityNot monotonic
2023-08-17T15:08:24.592944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412.71 3
 
0.1%
517.43 3
 
0.1%
402.66 3
 
0.1%
404.2 2
 
0.1%
494.15 2
 
0.1%
468.37 2
 
0.1%
415.01 2
 
0.1%
392.7 2
 
0.1%
411.3 2
 
0.1%
351.48 2
 
0.1%
Other values (1924) 1984
98.9%
ValueCountFrequency (%)
201.62 1
< 0.1%
210.32 1
< 0.1%
233.91 1
< 0.1%
245.86 1
< 0.1%
252.97 1
< 0.1%
254.39 2
0.1%
257.01 1
< 0.1%
257.7 1
< 0.1%
258.88 1
< 0.1%
259.96 1
< 0.1%
ValueCountFrequency (%)
753.34 1
< 0.1%
708.23 1
< 0.1%
695.37 1
< 0.1%
669.73 1
< 0.1%
666.69 1
< 0.1%
657.57 1
< 0.1%
656.92 1
< 0.1%
652.54 1
< 0.1%
649.81 1
< 0.1%
646.73 1
< 0.1%

Organic_carbon
Real number (ℝ)

Distinct1028
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.366153
Minimum2.2
Maximum27.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:24.683823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.953
Q112.13
median14.33
Q316.69
95-th percentile19.64
Maximum27.01
Range24.81
Interquartile range (IQR)4.56

Descriptive statistics

Standard deviation3.3219741
Coefficient of variation (CV)0.23123616
Kurtosis0.039087648
Mean14.366153
Median Absolute Deviation (MAD)2.26
Skewness-0.021355161
Sum28832.87
Variance11.035512
MonotonicityNot monotonic
2023-08-17T15:08:24.764239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.35 8
 
0.4%
13.79 7
 
0.3%
12.07 6
 
0.3%
16.14 6
 
0.3%
14.25 6
 
0.3%
15.67 6
 
0.3%
12.49 6
 
0.3%
18.02 5
 
0.2%
14.98 5
 
0.2%
11.67 5
 
0.2%
Other values (1018) 1947
97.0%
ValueCountFrequency (%)
2.2 1
< 0.1%
4.37 1
< 0.1%
4.47 1
< 0.1%
4.86 1
< 0.1%
4.97 1
< 0.1%
5.16 1
< 0.1%
5.19 1
< 0.1%
5.2 1
< 0.1%
5.22 1
< 0.1%
5.32 1
< 0.1%
ValueCountFrequency (%)
27.01 1
< 0.1%
24.76 1
< 0.1%
23.92 1
< 0.1%
23.6 1
< 0.1%
23.57 1
< 0.1%
23.4 1
< 0.1%
23.37 1
< 0.1%
23.32 1
< 0.1%
23.23 1
< 0.1%
23.14 1
< 0.1%

Trihalomethanes
Real number (ℝ)

Distinct1686
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.405057
Minimum8.58
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:24.861978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.58
5-th percentile39.585
Q155.955
median66.54
Q377.31
95-th percentile91.659
Maximum124
Range115.42
Interquartile range (IQR)21.355

Descriptive statistics

Standard deviation16.08709
Coefficient of variation (CV)0.242257
Kurtosis0.22234742
Mean66.405057
Median Absolute Deviation (MAD)10.66
Skewness-0.051624408
Sum133274.95
Variance258.79447
MonotonicityIncreasing
2023-08-17T15:08:25.161083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.91 4
 
0.2%
55.4 4
 
0.2%
63.7 4
 
0.2%
42.29 3
 
0.1%
73.19 3
 
0.1%
81.39 3
 
0.1%
52.42 3
 
0.1%
47.1 3
 
0.1%
62.32 3
 
0.1%
66.69 3
 
0.1%
Other values (1676) 1974
98.4%
ValueCountFrequency (%)
8.58 1
< 0.1%
14.34 1
< 0.1%
15.68 1
< 0.1%
16.29 1
< 0.1%
17.53 1
< 0.1%
17.92 1
< 0.1%
18.02 1
< 0.1%
19.18 1
< 0.1%
22.22 1
< 0.1%
23.14 1
< 0.1%
ValueCountFrequency (%)
124 1
< 0.1%
120.03 1
< 0.1%
116.16 1
< 0.1%
114.21 1
< 0.1%
114.03 1
< 0.1%
113.05 1
< 0.1%
112.62 1
< 0.1%
111.6 1
< 0.1%
111.12 1
< 0.1%
110.43 1
< 0.1%

Turbidity
Real number (ℝ)

Distinct362
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9700897
Minimum1.45
Maximum6.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-08-17T15:08:25.255054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile2.69
Q13.44
median3.97
Q34.515
95-th percentile5.2
Maximum6.49
Range5.04
Interquartile range (IQR)1.075

Descriptive statistics

Standard deviation0.77995117
Coefficient of variation (CV)0.19645681
Kurtosis-0.045387937
Mean3.9700897
Median Absolute Deviation (MAD)0.53
Skewness-0.033571951
Sum7967.97
Variance0.60832382
MonotonicityNot monotonic
2023-08-17T15:08:25.333304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.92 20
 
1.0%
3.63 17
 
0.8%
4.18 17
 
0.8%
4.37 16
 
0.8%
4.24 16
 
0.8%
4.59 15
 
0.7%
3.42 15
 
0.7%
3.7 15
 
0.7%
3.08 14
 
0.7%
4.1 14
 
0.7%
Other values (352) 1848
92.1%
ValueCountFrequency (%)
1.45 1
< 0.1%
1.49 1
< 0.1%
1.5 1
< 0.1%
1.68 1
< 0.1%
1.81 1
< 0.1%
1.84 1
< 0.1%
1.87 1
< 0.1%
1.91 1
< 0.1%
1.92 1
< 0.1%
1.96 2
0.1%
ValueCountFrequency (%)
6.49 2
0.1%
6.39 1
< 0.1%
6.36 1
< 0.1%
6.31 1
< 0.1%
6.23 1
< 0.1%
6.08 1
< 0.1%
6.06 1
< 0.1%
6.03 1
< 0.1%
5.99 2
0.1%
5.96 1
< 0.1%

Potability
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1196 
1
811 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Length

2023-08-17T15:08:25.411004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T15:08:25.489408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Most occurring characters

ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2007
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
59.6%
1 811
40.4%

Interactions

2023-08-17T15:08:22.692702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:16.935951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.710558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.371881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.042179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.818496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.506579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.195137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.857356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.760357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.120492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.791221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.449163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.112075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.898032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.572846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.272573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.923817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.843135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.191102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.864915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.521010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.175482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.971436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.662750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.344272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.009385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.918169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.267954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.928291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.589813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.377395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.040379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.729946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.407777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.239682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.989239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.342599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.011480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.664781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.441423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.124043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.816988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.490078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.309938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:23.068197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.419858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.086546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.745692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.526182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.190213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.896431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.560039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.391695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:23.139160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.491061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.160677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.823217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.594809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.273015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.976086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.640170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.470171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:23.209984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.558739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.228883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.893016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.673870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.349621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.042172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.707886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.540253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:23.274337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:17.645341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.303351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:18.956855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:19.747821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:20.432014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.123203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:21.781801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-17T15:08:22.607344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-08-17T15:08:25.552473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
ph1.0000.138-0.080-0.0400.0150.0100.0240.018-0.0470.097
Hardness0.1381.000-0.050-0.020-0.0970.0020.012-0.022-0.0290.074
Solids-0.080-0.0501.000-0.041-0.1430.0050.004-0.0220.0260.046
Chloramines-0.040-0.020-0.0411.0000.023-0.021-0.0230.0150.0000.076
Sulfate0.015-0.097-0.1430.0231.000-0.0200.016-0.027-0.0130.148
Conductivity0.0100.0020.005-0.021-0.0201.0000.017-0.0060.0220.000
Organic_carbon0.0240.0120.004-0.0230.0160.0171.000-0.004-0.0110.000
Trihalomethanes0.018-0.022-0.0220.015-0.027-0.006-0.0041.000-0.0230.000
Turbidity-0.047-0.0290.0260.000-0.0130.022-0.011-0.0231.0000.000
Potability0.0970.0740.0460.0760.1480.0000.0000.0000.0001.000

Missing values

2023-08-17T15:08:23.377861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-17T15:08:23.494426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
05.80193.2019451.774.15255.98365.4814.928.582.181
17.78196.8224789.356.55331.04372.7612.0714.345.050
26.95214.1732946.575.48333.44318.8812.8115.684.930
38.28227.6517995.417.49323.38459.8714.3616.293.690
47.06191.5516473.078.44367.85462.9912.5717.533.941
56.39213.0220965.485.38327.65369.3413.7617.923.920
66.64215.0616488.056.64304.76507.1311.9818.024.711
76.25163.2226408.886.03429.02509.9623.5719.185.041
86.26270.478572.429.92286.33490.9412.9322.224.750
97.18201.0825234.435.22283.74384.0112.4323.143.670
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
19978.37179.5222022.635.22339.49396.7013.70110.432.790
19984.63208.9129307.136.13304.03456.2110.82111.124.750
19997.31193.4719343.157.66306.69426.5612.84111.604.050
20009.16186.6715797.038.15333.81425.7512.18112.624.531
20016.34164.0726594.357.38338.43607.1014.93113.054.581
20028.29151.5714402.739.05303.08322.5213.65114.034.271
20038.97195.749049.687.47396.45378.5317.76114.213.980
20045.04190.1629258.744.99300.48332.3611.06116.163.531
20056.15197.5439657.279.90288.16319.4311.59120.034.600
20067.90210.7315896.376.91319.89448.6718.17124.002.851